Voya Financial, SVP, Head of Data: Start-Ups That Do Data Well Succeed at Much Higher Rate

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CDO Mag Julia Bardmesser Part 1 of 3

(US and Canada) Julia Bardmesser, SVP, Head of Data, Architecture and Salesforce Development, Voya Financial, speaks with Jason Masker, Chief Technology Officer at Field Stratascale, discussing data as an asset and the evaluation of data from multiple dimensions.

Bardmesser opens the discussion with the phrase "data as an asset," noting how diversely the phrase has been used for a decade. "Data is the only asset, as all other assets express themselves through data," she says.

Speaking on the organizational use of data as an asset, Bardmesser addresses the concern of the evaluation of data. "If you have an asset, you do want to have some way of evaluation," she says, further explaining how data about customers, products, or customers’ transactions all form an asset.

Bardmesser, however, has her own way of evaluating data — a three-dimensional approach — where she divides data value into three categories. The first category is content, which implies having optimum knowledge about the data in the form of people, products, dealer organizations, operations, and finances.

The data assets, however, need to have the capability to be used. This is where the second dimension of data — capability — starts. "You may have all of this data but if you have an old mainframe system that doesn't have a relational database that somebody needs, you can't use it,” she points out.

According to Bardmesser, the data capability factor brings the data together and makes it sensible enough to be used in the decision-making process. The third and the most important category for evaluating data as an asset is people and culture. She says that data cannot be an asset without subject matter expertise, cross-organizational data culture, or people who understand the importance of data and metadata.

"I think those are three very important pieces that together allow a company to use data as an asset in multiple ways," she says.

When a company invests in shares or buys products, Bardmesser continues, they are implicitly buying data. Whereas data capability is measured with regard to decoding the usability of different data components and how they can be brought together, resulting in cost reduction for companies. Bringing two companies together after evaluating data capabilities helps with overall savings implicitly, without explicitly considering its role in business growth, she explains.

Bardmesser maintains, however, that the third category — people and talent — remains unnoticed by companies. "Startups of any type that do data well succeed at a much higher rate,” she says. And having the right knowledge about any company’s data infrastructure communicating with people is the key to understanding how important data is to them. Because, she notes, the acceleration of the company depends on the value people and culture bring in.

Netflix and Google, for example, have advanced while leveraging data. But many executive MBA programs have no mention of data management, she points out. While she understands that an MBA, being a business course, cannot teach Python code, she believes that the inclusion of data management in such courses would help businesses understand deata’s importance and resolve the challenges rising out of it. In fact, she considers it to be a key concern that has to be addressed.

Founders and leaders, Bardmesser says, should have knowledge of data management so that a data-first culture can be built so that data-based decisions are made, hastening growth by evolving services provided.

Addressing the complexities of work in large, diverse organizations, she maintains that it is because the acquisition happened but integration did not. "We've acquired and never really integrated. Why didn't we integrate? Because it was hard, because they probably bought something without a data capability, without an API platform," she says.

Hence, the hardship of integrating data and the inability to invest in data management in the past have led to data sets with no master data management. Bardmesser says this is one of the major problem statements around data.

Companies with good data capabilities, however, will have improved integration costs and derive value faster. Also, if a company has a team with incredible data capabilities, it becomes easier for them to create better footprints.

A company’s data capability evaluation should not hide under technology in case the company is cloud-native, she concludes.